hand-tracking

hand-tracking

Purpose

This skill enables real-time detection and tracking of hand gestures in AR/VR environments using computer vision algorithms, allowing for seamless integration into applications like virtual interactions or gesture-based controls.

When to Use

Use this skill when building AR/VR apps that require hand gesture input, such as gesture-controlled interfaces in gaming, remote collaboration tools, or accessibility features in virtual reality headsets. Apply it in scenarios with live camera feeds where low-latency tracking is essential, like real-time object manipulation.

Key Capabilities

• Real-time hand pose estimation with up to 21 key points per hand using pre-trained models like MediaPipe Hands.
• Gesture recognition for common actions (e.g., pinch, wave, swipe) with configurable thresholds for accuracy.
• Support for multiple input sources, including webcam streams or AR/VR device cameras, with frame rates up to 60 FPS.
• Output formats including JSON for hand landmarks and event triggers for detected gestures.
• Customizable models via config files, such as specifying minimum confidence levels (e.g., 0.5 for detection).

Usage Patterns

Always initialize the skill with an input source and authentication. Start by setting the environment variable for API access, e.g., export OPENCLAW_API_KEY=your_api_key. For CLI usage, pipe input from a camera device. In code, import the skill as a module and call tracking functions in a loop. Handle asynchronous operations to avoid blocking the main thread. For AR/VR integration, combine with rendering loops to update virtual objects based on hand positions.